Ultrasonic doppler flow imaging method

Abstract

Disclosed is an ultrasonic Doppler flow imaging method. The method includes: using eigen decomposition on an ultrasonic image to obtain eigenvectors matrices with different power values, so that the SNR and power distribution of each point of the ultrasonic image are calculated, thereby accurately extracting flow data with high reliability and obtaining accurate and stable flow images. The method has the beneficial effects of filtering out tissue signals and noise signals on the characteristic dimension, so as to better extract flow data by using eigen decomposition on ultrasonic images, retaining complete flow data and providing stable and clear imaging of fine flow without the need of contrast agent injection that may cause injuries to human bodies.

What is claimed is: 
     
         1 . An ultrasonic Doppler flow imaging method, comprising:
 S01, using eigen decomposition on an ultrasonic image from ultrasound scan to obtain N eigenvectors matrices, and enveloping data of the N eigenvectors matrices to obtain power of the N eigenvectors matrices, which are: high-power eigenvectors matrices, medium-power eigenvectors matrices and low-power eigenvectors matrices respectively; wherein, the N/5 matrices with highest power values are selected from the N eigenvectors matrices as high-power eigenvectors matrix, the N/10 eigenvectors matrices with the lowest power values are selected from the N eigenvectors matrices as low-power eigenvectors matrix, and the rest are “medium-power eigenvectors matrix”;   S02, determining the point distribution of the ultrasound image in each eigenvectors matrix, and retaining the points mainly distributed in the medium-power eigenvectors matrices based on the data of the N eigenvectors matrices obtained in step S01;   S03, taking the data of the low-power eigenvectors matrices in step S01 as noise signals, and calculating a signal-to-noise ratio (SNR) of each point of the ultrasonic image, the points with SNR lower than the set value are considered as points with low reliability and eliminated, and the points with SNR higher than the set value are considered as points with high reliability and retained; and   S04, combining the results of steps S02 and S03, extracting the points complying with main distribution in the medium-power eigenvectors matrices in step S02 and high reliability in step S03, thereby obtaining flow image data with high reliability.   
     
     
         2 . The ultrasonic Doppler flow imaging method according to  claim 1 , further comprising step S05, processing the reliable flow image data by using the opening and closing function with an X-Shape element for reconnecting the broken parts and rejecting background noise, so as to obtain continuous clear flow image data. 
     
     
         3 . The ultrasonic Doppler flow imaging method according to  claim 2 , further comprising step S06, calculating the continuous clear flow image data from step S05 for an area of connected components, with the points where the area of connected components less than the set value regarded as a singularity point, and obtaining continuous clear and noise-free flow image data after eliminating the singularity point.